The Role of Generative AI in Diagnostic Radiology: Accuracy, Ethics, and Clinical Integration

Abstract

Generative artificial intelligence (AI) tools are transforming diagnostic radiology by providing capabilities for high-clarity imaging, augmentation, and interpretation. Generative AI models (e.g. generative adversarial networks (GANs) and diffusion models) can create realistic medical images and even aid the detection, segmentation, and treatment process of diseases.This research focuses on the accuracy, ethics, and feasibility of generative AI with respect to its potential practice-enhancing role in diagnostic radiology. A systematic literature review approach was applied, using databases such as PubMed, Scopus, IEEE Xplore and Web of Science for the years from 2010 to 2025. We studied research involving the use of generative artificial intelligence in radiology, focusing on diagnostic accuracy, assessments of ethics, and clinical readiness. In low-data settings, generative models have shown significant progress in producing realistic images and providing diagnostic prediction accuracy. Nonetheless, the ethical issues of data privacy, bias, and explainability continue to exist. Regulatory, technical, and educational barriers continue to constrain integration into clinical workflows. Generative AI has the potential to be a game-changer in radiology, but its implementation needs extensive planning to solve ethical, regulatory, and infrastructural barriers. Using Generative AI in Diagnostic Radiology: A Brief Review of GANs and Their Real-World Applications in Clinical Settings